Analysis of a scene captured by a camera often involves classifying the objects that appear in the scene.
A classification of an object appearing in a scene is often related to classifications of other objects appearing in the scene. For example, an indoors office scene usually contains objects that would be classified differently to objects found in an outdoors forest scene. Therefore, it is desirable to exploit the correlation between classifications of objects appearing within a scene.
One type of approach to exploiting such correlations is to use a probabilistic model of a scene, where some discrete random variables in the probabilistic model each represent a classification of an object in a scene. Each variable has a set of possible states, where each state corresponds to a possible classification of the object in the scene corresponding to the variable. A probabilistic model specifies a joint probability distribution over possible combinations of classifications of the objects in the scene, thus modelling the correlation between classifications of the objects. The objects may be classified using a probabilistic model by finding the most probable classifications of the objects according to the joint probability distribution of the probabilistic model. Equivalently, the correlations between classifications of objects appearing in the scene may be expressed as a combinatorial optimization problem such as energy minimization, and the objects classified by finding an optima of the combinatorial optimization problem. In the following text, the term probabilistic models should be considered to include equivalent combinatorial optimization formulations.
When multiple similar scenes are captured by a camera, a probabilistic model may be applied to each scene individually. In this case, as the scenes are similar they may yield similar probabilistic models. Finding the most probable classifications of objects according to a probabilistic model is often computationally expensive. It is therefore desirable to exploit the similarity between the probabilistic models in order to reduce the computation time.
One approach to exploit the similarity between two probabilistic models is to associate each variable in a first probabilistic model with one or more variables in a second probabilistic model. A message-passing algorithm, called belief propagation, can be used to solve the first probabilistic model. An intermediate result of the message-passing algorithm can be transferred from the first probabilistic model to a message-passing algorithm used to solve the second probabilistic model. However, this approach requires that the variables of the second probabilistic model have the same set of possible states as each associated variable of the first probabilistic model. Also, the message-passing algorithm may find only approximate optima for a probabilistic model, which is sub-optimal. Many object classification tasks do not achieve a desired level of accuracy when sub-optimal solutions are provided for a probabilistic model.
A second approach to exploit the similarity between two probabilistic models is to transform each probabilistic model into an equivalent flow network, such that finding the maximum flow solution to the flow network is equivalent to finding the most probable state in the probabilistic model. A first maximum flow solution to a first flow network for the first probabilistic model can then be used to initialize a second flow network solution for a second flow network for the second probabilistic model. However, this approach requires a one-to-one mapping from variables and states of the first probabilistic model to variables and states of the second probabilistic model, and further requires constructing a pair of equivalent flow networks for each of the probabilistic models. This is possible for only a restricted class of probabilistic models that have sub-modular potential functions. Many object classification tasks cannot use sub-modular potential functions.
Thus, there exists a need for an improved method for exploiting the similarity between probabilistic models to reduce the computational cost of solving multiple similar probabilistic models. Also, any solution should desirably operate with probabilistic models that do not have sub-modular potential functions, and where sub-optimal solutions are insufficient.